IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-09-00423.html
   My bibliography  Save this article

Profitability of the On-Balance Volume Indicator

Author

Listed:
  • William Wai Him Tsang

    (Department of Economics, The Chinese University of Hong Kong)

  • Terence Tai Leung Chong

    (Department of Economics, The Chinese University of Hong Kong)

Abstract

In the literature, there is a lack of empirical studies documenting the profitability of volume-based technical indicators. This paper evaluates the profitability of the On-Balance Volume (OBV) trading rule. Our result shows that the OBV trading rule is increasingly profitable and rewards investors with notable returns in the stock markets of Greater China.

Suggested Citation

  • William Wai Him Tsang & Terence Tai Leung Chong, 2009. "Profitability of the On-Balance Volume Indicator," Economics Bulletin, AccessEcon, vol. 29(3), pages 2424-2431.
    • Handle: RePEc:ebl:ecbull:eb-09-00423
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2009/Volume29/EB-09-V29-I3-P87.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas Shik & Terence Tai-Leung Chong, 2007. "A comparison of MA and RSI returns with exchange rate intervention," Applied Economics Letters, Taylor & Francis Journals, vol. 14(5), pages 371-383.
    2. Ben Marshall & Martin Young & Rochester Cahan, 2008. "Are candlestick technical trading strategies profitable in the Japanese equity market?," Review of Quantitative Finance and Accounting, Springer, vol. 31(2), pages 191-207, August.
    3. Chong, Terence Tai-Leung & Ip, Hugo Tak-Sang, 2009. "Do momentum-based strategies work in emerging currency markets?," Pacific-Basin Finance Journal, Elsevier, vol. 17(4), pages 479-493, September.
    4. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    5. Treynor, Jack L & Ferguson, Robert, 1985. "In Defense of Technical Analysis," Journal of Finance, American Finance Association, vol. 40(3), pages 757-773, July.
    6. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    7. Hudson, Robert & Dempsey, Michael & Keasey, Kevin, 1996. "A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices - 1935 to 1994," Journal of Banking & Finance, Elsevier, vol. 20(6), pages 1121-1132, July.
    8. Mills, Terence C, 1997. "Technical Analysis and the London Stock Exchange: Testing Trading Rules Using the FT30," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 2(4), pages 319-331, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Senol Emir & Hasan Dincer & Umit Hacioglu & Serhat Yuksel, 2016. "Random Regression Forest Model using Technical Analysis Variables: An application on Turkish Banking Sector in Borsa Istanbul (BIST)," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(3), pages 85-102, April.
    2. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
    3. Seung Hwan Jeong & Hee Soo Lee & Hyun Nam & Kyong Joo Oh, 2021. "Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market," Sustainability, MDPI, vol. 13(3), pages 1-16, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chong, Terence Tai-Leung & Lam, Tau-Hing & Yan, Isabel Kit-Ming, 2012. "Is the Chinese stock market really inefficient?," China Economic Review, Elsevier, vol. 23(1), pages 122-137.
    2. Terence Tai-Leung Chong & Wing-Kam Ng & Venus Khim-Sen Liew, 2014. "Revisiting the Performance of MACD and RSI Oscillators," JRFM, MDPI, vol. 7(1), pages 1-12, February.
    3. Skouras, Spyros, 2001. "Financial returns and efficiency as seen by an artificial technical analyst," Journal of Economic Dynamics and Control, Elsevier, vol. 25(1-2), pages 213-244, January.
    4. Terence Tai-Leung Chong & Chen Li & Ho Tin Yu, 2008. "Structural Change in the Stock Market Efficiency after the Millennium: The MACD Approach," Economics Bulletin, AccessEcon, vol. 7(12), pages 1-6.
    5. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    6. Chen, Cheng-Wei & Huang, Chin-Sheng & Lai, Hung-Wei, 2009. "The impact of data snooping on the testing of technical analysis: An empirical study of Asian stock markets," Journal of Asian Economics, Elsevier, vol. 20(5), pages 580-591, September.
    7. Lu, Tsung-Hsun, 2014. "The profitability of candlestick charting in the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 65-78.
    8. R. Rosillo & D. de la Fuente & J. A. L. Brugos, 2013. "Technical analysis and the Spanish stock exchange: testing the RSI, MACD, momentum and stochastic rules using Spanish market companies," Applied Economics, Taylor & Francis Journals, vol. 45(12), pages 1541-1550, April.
    9. Alexandros E. Milionis & Evangelia Papanagiotou, 2008. "A Note on the Use of Moving Average Trading Rules to Test For Weak from Efficiency in Capital Markets," Working Papers 91, Bank of Greece.
    10. Trifan, Emanuela, 2004. "Entscheidungsregeln und ihr Einfluss auf den Aktienkurs," Darmstadt Discussion Papers in Economics 131, Darmstadt University of Technology, Department of Law and Economics.
    11. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    12. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    13. Tania Morris & Jules Comeau, 2020. "Portfolio creation using artificial neural networks and classification probabilities: a Canadian study," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 133-163, June.
    14. Wong, Wing-Keung & Du, Jun & Chong, Terence Tai-Leung, 2005. "Do the technical indicators reward chartists? A study on the stock markets of China, Hong Kong and Taiwan," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 1(2), pages 1-23.
    15. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    16. Michael McAleer & John Suen & Wing Keung Wong, 2016. "Profiteering from the Dot-Com Bubble, Subprime Crisis and Asian Financial Crisis," The Japanese Economic Review, Japanese Economic Association, vol. 67(3), pages 257-279, September.
    17. Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.
    18. Stefanescu, Răzvan & Dumitriu, Ramona, 2015. "Buy and sell signals on Bucharest Stock Exchange," MPRA Paper 89014, University Library of Munich, Germany, revised 05 Jan 2016.
    19. Alexandros Milionis & Evangelia Papanagiotou, 2009. "A study of the predictive performance of the moving average trading rule as applied to NYSE, the Athens Stock Exchange and the Vienna Stock Exchange: sensitivity analysis and implications for weak-for," Applied Financial Economics, Taylor & Francis Journals, vol. 19(14), pages 1171-1186.
    20. Alexandros E. Milionis & Evangelia Papanagiotou, 2013. "Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non-linear dependencies in stock returns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2480-2494, November.

    More about this item

    Keywords

    On-Balance Volume; Moving Average; Market Efficiency.;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G0 - Financial Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ebl:ecbull:eb-09-00423. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: John P. Conley (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.